AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks

Roque Lopez, Raoni Lourenco, Remi Rampin, Sonia Castelo, Aécio S. R. Santos, Jorge Henrique Piazentin Ono, Claudio Silva, Juliana Freire
Proceedings of the Second International Conference on Automated Machine Learning, PMLR 224:22/1-22, 2023.

Abstract

We present AlphaD3M, an open-source Python library that supports a wide range of machine learning tasks over different data types. We discuss the challenges involved in supporting multiple tasks and how AlphaD3M addresses them by combining deep reinforcement learning and meta-learning to effectively construct pipelines over a large collection of primitives. To better integrate the use of AutoML within the data science lifecycle, we have built an ecosystem of tools around AlphaD3M that support user-in-the loop tasks, including the selection of suitable pipelines and the development of solutions for complex systems. We present use cases that demonstrate some of these features. We report the results of detailed experimental evaluations which show that AlphaD3M is effective and derives high-quality pipelines for a diverse set of problems with performance that is comparable or superior to state-of-the-art AutoML systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v224-lopez23a, title = {AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks}, author = {Lopez, Roque and Lourenco, Raoni and Rampin, Remi and Castelo, Sonia and Santos, A\'ecio S. R. and Ono, Jorge Henrique Piazentin and Silva, Claudio and Freire, Juliana}, booktitle = {Proceedings of the Second International Conference on Automated Machine Learning}, pages = {22/1--22}, year = {2023}, editor = {Faust, Aleksandra and Garnett, Roman and White, Colin and Hutter, Frank and Gardner, Jacob R.}, volume = {224}, series = {Proceedings of Machine Learning Research}, month = {12--15 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v224/lopez23a/lopez23a.pdf}, url = {https://proceedings.mlr.press/v224/lopez23a.html}, abstract = {We present AlphaD3M, an open-source Python library that supports a wide range of machine learning tasks over different data types. We discuss the challenges involved in supporting multiple tasks and how AlphaD3M addresses them by combining deep reinforcement learning and meta-learning to effectively construct pipelines over a large collection of primitives. To better integrate the use of AutoML within the data science lifecycle, we have built an ecosystem of tools around AlphaD3M that support user-in-the loop tasks, including the selection of suitable pipelines and the development of solutions for complex systems. We present use cases that demonstrate some of these features. We report the results of detailed experimental evaluations which show that AlphaD3M is effective and derives high-quality pipelines for a diverse set of problems with performance that is comparable or superior to state-of-the-art AutoML systems.} }
Endnote
%0 Conference Paper %T AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks %A Roque Lopez %A Raoni Lourenco %A Remi Rampin %A Sonia Castelo %A Aécio S. R. Santos %A Jorge Henrique Piazentin Ono %A Claudio Silva %A Juliana Freire %B Proceedings of the Second International Conference on Automated Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Aleksandra Faust %E Roman Garnett %E Colin White %E Frank Hutter %E Jacob R. Gardner %F pmlr-v224-lopez23a %I PMLR %P 22/1--22 %U https://proceedings.mlr.press/v224/lopez23a.html %V 224 %X We present AlphaD3M, an open-source Python library that supports a wide range of machine learning tasks over different data types. We discuss the challenges involved in supporting multiple tasks and how AlphaD3M addresses them by combining deep reinforcement learning and meta-learning to effectively construct pipelines over a large collection of primitives. To better integrate the use of AutoML within the data science lifecycle, we have built an ecosystem of tools around AlphaD3M that support user-in-the loop tasks, including the selection of suitable pipelines and the development of solutions for complex systems. We present use cases that demonstrate some of these features. We report the results of detailed experimental evaluations which show that AlphaD3M is effective and derives high-quality pipelines for a diverse set of problems with performance that is comparable or superior to state-of-the-art AutoML systems.
APA
Lopez, R., Lourenco, R., Rampin, R., Castelo, S., Santos, A.S.R., Ono, J.H.P., Silva, C. & Freire, J.. (2023). AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks. Proceedings of the Second International Conference on Automated Machine Learning, in Proceedings of Machine Learning Research 224:22/1-22 Available from https://proceedings.mlr.press/v224/lopez23a.html.

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